International Journal of Communication Systems最新文献

Severe resource constraints, multi-hop connections, variable topology, and high sensitivity of transmitted data have raised the issue of quality of service (QoS) as one of the serious issues for wireless body area networks (WBANs). Therefore, many papers have been introduced in this field to improve the important aspects of the QoS field. However, studies indicate that some important issues have not been well considered, the most important of which are the lack of measures to consider the dynamic conditions of nodes and the effective support for varying traffic QoS. In addition, the performance of most methods leads to a sharp increase in control overhead. In this paper, a QoS-Temperature-Aware energy-efficient Adaptive Routing (QTAEEAR) based on the development of the Q-learning algorithm has been introduced. QTAEEAR is a three-step method. The routing table is created and updated in the first step. In the second step, routing and selecting the next-hop node are performed based on the Q learning. In the third step, learning is updated in response to new network conditions. Simulation results using NS-2 indicated the optimal performance and superiority of QTAEEAR compared to other similar methods.

software-defined networking (SDN) offers flexible traffic management but remains vulnerable to sophisticated cyberattacks, necessitating accurate and efficient network traffic detection. Existing SDN-based intrusion detection systems often suffer from high computational cost, poor scalability, and reduced accuracy in high-throughput or encrypted environments. To address these limitations, the NTD-SDN-RICCNN framework is proposed, which integrates fast robust iterative filtering (FRIF) for noise removal with spectral graph fast Fourier transform (SGFFT) for discriminative feature extraction. Rotation-invariant coordinate convolutional neural network (RICCNN) optimized with weighted velocity-guided gray wolf optimizer (WVGWO) for parameter tuning. The proposed method reduces redundant feature processing while improving detection accuracy and inference speed. Experiments on the SDN intrusion detection dataset show that NTD-SDN-RICCNN attains 99.7% accuracy, 99.6% precision, 99.5% recall, and reduces computational time by up to 32.5% compared to the state-of-the-art baselines. These results demonstrate the method's effectiveness and scalability for real-time SDN intrusion detection in diverse network conditions.

In wireless sensor networks (WSN), the sensor nodes are deployed randomly where the sensor nodes are not positioned away from each other. The intersection of sensing ranges creates an overlapping area. Every sharing node perceives the same event and generates redundant and associated data if it takes place inside the overlapping area. In this paper, a multimetric-based optimization of cluster selection and data redundancy elimination through copula variational LSTM in wireless sensor networks (CS-DRE-CVLSTM-WSN) is proposed. Initially, cluster formation using semantic invariant multiview clustering (SIMVC) for data aggregation in WSN by leveraging data characteristics and connectivity patterns is discussed. Then, the clusters formed are given to the Wader Hunt Optimization Algorithm (WHOA) for cluster head selection. For this, a novel method to enhance data redundancy elimination efficiency, considering factors, like trust degree computation, energy efficiency, link quality, path loss, distance from the target node to the base station, and aggregation delay is proposed. The selected clusters are fed into copula variational LSTM (CV-LSTM) to optimize cluster selection and predict temporal trends in key network metrics. The offline meta RL (OMRL) framework is proposed to suppress redundant data streams and decide which data to transmit or aggregate. The reward system assigns positive values for efficient aggregation and reduced communication costs, while penalizing data loss or unnecessary transmissions. The output is a set of policies for effective data aggregation and redundancy elimination. The performance of the proposed CS-DRE-CVLSTM-WSN method is evaluated with existing methods like the reliable cluster dependent data aggregation scheme for IoT with hybrid deep learning methods (RC-DAS-IoT-HDL), new machine learning-driven data aggregation for predicting data redundancy in IoT connected WSN (ML-PDR-IoT-WSN), and two vector data prediction techniques for energy-efficient data aggregation in WSN (TVDP-EEDA-WSN), respectively.

This paper investigates an energy-limited multi-user mixed radio frequency/free space optical (RF/FSO) system, where the RF links and FSO link undergo Rayleigh fading and Gamma-Gamma turbulence models incorporating the effect of pointing errors, respectively. Simultaneous wireless information and power transfer (SWIPT) based on the power splitting protocol is employed to maintain communication continuity of the system operating in an energy-limited scenario. Moreover, an energy-based user selection strategy is adopted to enhance the overall performance. Closed-form expressions for outage probability (OP) and approximate bit error rate (BER) are provided by using Meijer-G functions. The accuracy of theoretical derivations is validated by Monte-Carlo simulations. Results demonstrate that, under the user selection strategy, merely increasing the number of users cannot significantly boost system performance. Furthermore, influenced by multiple factors, the prudent design of the power splitting proportion enables system performance optimization.

The quick propagation of Internet of Things (IoT) devices in 6th Generation (6G) networks intensifies security challenges due to high-dimensional and diverse nature of IoT data, which complicates feature selection and increases computational overhead. Dynamic and evolving attack patterns, including various intrusion types, malware, denial-of-service attempts, and coordinated botnet attacks, further reduce detection reliability. To address these challenges, Optimized Periodic Implicit Generative Adversarial networks with advanced Transformer (OPIGAT) proposes an end-to-end framework for robust and scalable IoT security. The framework begins with a preprocessing stage that handles missing values, outliers, and normalization for clean and consistent data. Feature optimization occurs through a Hybrid Tuna Particle Swarm Optimization Algorithm (HTPSO), which Merges Particle Swarm Optimization (PSO) global search capability with Tuna Swarm Optimization (TSO) spiral foraging-inspired local refinement, enabling precise selection of compact and highly discriminative features while reducing dimensionality. OPIGAT classifier detects diverse attacks, with the generative component synthesizing realistic traffic patterns and the transformer module capturing contextual relationships, enhancing anomaly detection and reducing false positives. Finally, a lightweight blockchain integrated with InterPlanetary File System (IPFS) ensures secure and scalable data management, employing proof of authority, Elliptic Curve Cryptography (ECC) with Ring signature encryption, and smart contract–based revocation to maintain privacy, integrity, and efficiency. Extensive experiments on CICIoT-2023 and ROUT-4-2023 datasets demonstrate superior accuracy (98.78% and 97%), high detection efficiency (98.21%), and a low false alarm rate (20%), while IPFS-enabled storage supports seamless scalability. These results establish OPIGAT as a secure, efficient, and highly effective solution for 6G IoT intrusion detection.

The nonlinear power amplifier (NPA) in a cognitive radio (CR) network can degrade sensing performance. While spectrum sensing primarily focuses on the receiver, modeling the transmitter's NPA is crucial to understand its impact on detection. This paper investigates spectrum sensing performance in CR networks assisted by intelligent reconfigurable surfaces (IRS), considering the impact of NPA distortions. A novel multiple-input single-output (MISO) IRS-aided CR system, termed MICR, is proposed. The sensing performance of the proposed MICR system is analyzed in terms of the probabilities of false alarm and detection, considering the effect of IRS elements. The results are presented using receiver operating characteristic (ROC) curves. Furthermore, the impact of various system parameters such as the number of samples, number of IRS elements, IRS position, number of transmitting antennas, and phase shifts is analyzed. Additionally, the effects of line-of-sight (LoS) and non–line-of-sight (NLoS) propagation are evaluated. The ROC performance using different signal detectors is analyzed. The impact of interference, IRS overhead, and sensing energy on the proposed MICR system is also investigated. Extensive simulations demonstrate that IRS significantly improves the sensing performance of the CR network, both with and without NPA.

Performance issues with spectrum sensing are caused by multipath fading and shadowing, and obstacle blocking. This paper presents a novel Hybrid spectrum sensing framework that cooperatively combines energy detection (ED), matched filter detection (MFD), and cyclostationary feature detection (CFD) to detection reliability in cognitive radio networks. To mitigate fading and shadowing effects, cooperative spectrum sensing (CSS) is incorporated, leveraging multiple secondary users (SUs) for spatial diversity, achieving P_d = 0.80 at SNR = −20 dB using the OR fusion rule The key novelty lies in a dynamic switching mechanism that adaptively selects among ED, MFD, and CFD based on real-time signal and PU information availability, enabling operation under full, limited, or absent PU knowledge. Monte Carlo simulations in the low SNR region (−20 to −5 dB) demonstrate that the proposed hybrid detector achieves P_d values of 1, 0.98, and 0.95 for CFD, ED, and MFD, respectively, at P_f = 0.1. Moreover, it achieves energy efficiencies of 1.86 bits/J (non-CSS) and 2.05 bits/J (CSS), emphasizing the importance of energy-efficient spectrum access in power-constrained CR-IoT applications. The framework also minimizes average detection delay to 1.16 ms, ensuring faster PU identification. Owing to its adaptability and energy-aware operation, the proposed method is suitable for TV white spaces, CR-IoT, 5G-and-beyond networks, VANETs, and disaster-recovery communications.

Efficient resource allocation is vital for ensuring quality of service (QoS) in Long-Term Evolution (LTE) networks, especially for call admission control (CAC). This study introduces a multilevel bandwidth (BW) allocation model that dynamically assigns BW based on call requirements and computes the minimum necessary BW when availability is limited. Calls are classified as new calls (NC) or handoff calls (HC) based on network conditions. To enhance CAC decision-making, a cascade feedforward network (Cascade FFN), integration of a cascade neuro-fuzzy network and a deep feedforward neural network (DFNN), is employed. The proposed model achieves high BW utilization of 0.911, power efficiency of 65.098 dBm, and mean throughput of 516,830.584 bps. Also, it minimizes call blocking probabilities for NC, which is 0.591, and for HC, which is 0.605; the call dropping probabilities for NC, which is 0.606, and for HC, which is 0.592; the mean delay of 0.066 s; and the number of dropped users of 820.620. These results demonstrate significant improvements in LTE performance through intelligent BW allocation and CAC.

The efficient path selection of data in wireless sensor networks is vital for enhancing total system performance. Traditional path selection protocols encounter challenges related to frequent node movements, optimization of energy efficiency, and the nature of network environments. To tackle these challenges, an innovative methodology termed Deep reinforcement arctic puffin federated stochastic gradient learning (DRAFSGL) integrates federated learning and puffin optimization for energy-efficient multipath routing. Multiview deep embedded clustering (MDEC), enhanced by coyote and badger optimization into efficient clusters to reduce routing complexity. To ensure privacy, quantum-resistant homomorphic encryption (QRHE) enables secure computation without safeguarding against quantum threats. The data monitoring system applies game theory to optimize the behavior of agents involved in monitoring activities in the system. The suggested approach has exhibited extraordinary evaluation, attaining a peak accuracy of 99.7% alongside a precision of 98.9%, a specificity of 98.4%, a delay of 18 ms, a throughput of 87.9 Kbps, and a remarkable recall rate of 98.9%, as well as an F1-score when compared to existing methods. Overall, DRAFSGL methodology improves adaptive, secure, and energy-efficient multipath routing through the combination of federated learning and arctic puffin optimization. Meanwhile, the MDEC technique with improved coyote and badger optimization (ICBO) simplifies routing complexity and QRHE guarantees quantum-resistant, secure data transmission, effectively addressing the shortcomings of current techniques in dynamic 6G wireless sensor network (WSN)-based Internet of Things (IoT) systems.

The tactile internet of things (TIoT) demands ultralow latency, high reliability, and dynamic resource allocation for real-time human–machine interactions. Traditional network slicing methods (e.g., peak or average-based provisioning) fail to adapt to fluctuating haptic signals, causing inefficiencies like overprovisioning or underprovisioning. This paper proposes an intelligent in-content-aware elastic resource allocation framework for 6G-enabled TIoT, combining federated learning (FL) and dynamic network slicing to optimize resource allocation. FL enables distributed prediction of resource demands at tactile devices, preserving privacy and reducing bandwidth overhead. The framework dynamically adjusts network slices using an AI-driven close-loop feedback mechanism, improving elasticity. Building on prior FL-based architecture, this study further refines dynamic allocation for fluctuating tactile signals. Experiments show that the framework outperforms static methods, reducing packet loss by 17%, improving resource efficiency by 30%, and enhancing fairness under varying workloads. Key innovations include modeling TIoT signals with generalized Pareto–Poisson models, an end-to-end semantic orchestration workflow, and empirical validation of resilience to asynchronism and service level agreement (SLA) violations. The results highlight its potential to boost quality of service (QoS) and cost-efficiency in next-gen tactile applications.